AIMC Topic: Neoplasm Metastasis
Clear Filters Showing 101 to 110 of 133 articles
Cancer adjuvant chemotherapy strategic classification by artificial neural network with gene expression data: An example for non-small cell lung cancer.
Journal of biomedical informatics
May 18, 2015
PURPOSE: Adjuvant chemotherapy (ACT) is used after surgery to prevent recurrence or metastases. However, ACT for non-small cell lung cancer (NSCLC) is still controversial. This study aimed to develop prediction models to distinguish who is suitable f...
Kaplan-Meier Estimate
Chemotherapy, Adjuvant
Carcinoma, Non-Small-Cell Lung
Neoplasm Recurrence, Local
Neoplasm Metastasis
Proportional Hazards Models
Humans
Lung Neoplasms
Neural Networks, Computer
Computational Biology
Machine Learning
Gene Expression Regulation, Neoplastic
Treatment Outcome
Aged
Male
Middle Aged
Gene Expression Profiling
Algorithms
Prognosis
Female
Reproducibility of Results
Prevention from port-site metastasis.
Archives of gynecology and obstetrics
Apr 26, 2015
Port-site metastases in patients with gynecological cancer after robot-assisted operations.
Archives of gynecology and obstetrics
Feb 20, 2015
INTRODUCTION: Port-site metastasis is an extremely rare event in patients with cancer treated with robotic-assisted surgery. However, as robotic procedures are increasing, the incidence of port-site metastases might also increase. The purpose of our ...
Uncertainty quantification for deep learning-based metastatic lesion segmentation on whole body PET/CT.
Physics in medicine and biology
May 23, 2025
Deep learning models are increasingly being implemented for automated medical image analysis to inform patient care. Most models, however, lack uncertainty information, without which the reliability of model outputs cannot be ensured. Several uncerta...
Lexical associations can characterize clinical documentation trends related to palliative care and metastatic cancer.
Scientific reports
May 18, 2025
Palliative care is known to improve quality of life in advanced cancer. Natural language processing offers insights to how documentation around palliative care in relation to metastatic cancer has changed. We analyzed inpatient clinical notes using u...
A Deep Learning-Enabled Workflow to Estimate Real-World Progression-Free Survival in Patients With Metastatic Breast Cancer: Study Using Deidentified Electronic Health Records.
JMIR cancer
May 15, 2025
BACKGROUND: Progression-free survival (PFS) is a crucial endpoint in cancer drug research. Clinician-confirmed cancer progression, namely real-world PFS (rwPFS) in unstructured text (ie, clinical notes), serves as a reasonable surrogate for real-worl...
Incidence trends, overall survival, and metastasis prediction using multiple machine learning and deep learning techniques in pediatric and adolescent population with osteosarcoma and Ewing's sarcoma: nomogram and webpage.
Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
May 1, 2025
OBJECTIVE: The objective of this study was to analyze the incidence and overall survival (OS) of osteosarcoma (OSC) and Ewing's sarcoma (EWS) in a pediatric and adolescent population, employing machine learning (ML) and deep learning (DL) models to p...
MoLPre: A Machine Learning Model to Predict Metastasis of cT1 Solid Lung Cancer.
Clinical and translational science
Apr 1, 2025
Given that more than 20% of patients with cT1 solid NSCLC showed nodal or extrathoracic metastasis, early detection of metastasis is crucial and urgent for improving therapeutic planning and patients' risk stratification in clinical practice. This st...
Developing a Predictive Model for Metastatic Potential in Pancreatic Neuroendocrine Tumor.
The Journal of clinical endocrinology and metabolism
Dec 18, 2024
CONTEXT: Pancreatic neuroendocrine tumors (PNETs) exhibit a wide range of behavior from localized disease to aggressive metastasis. A comprehensive transcriptomic profile capable of differentiating between these phenotypes remains elusive.
Machine Learning-Driven Phenogrouping and Cardiorespiratory Fitness Response in Metastatic Breast Cancer.
JCO clinical cancer informatics
Sep 1, 2024
PURPOSE: The magnitude of cardiorespiratory fitness (CRF) impairment during anticancer treatment and CRF response to aerobic exercise training (AT) are highly variable. The aim of this ancillary analysis was to leverage machine learning approaches to...
Don't Miss the Future of Medicine
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 30978
- Neural Networks, Computer 29696
- Algorithms 26813
- Deep Learning 26324
- Artificial Intelligence 24267
- Robotics 13307
- Image Processing, Computer-Assisted 9512
- Retrospective Studies 9097
- Magnetic Resonance Imaging 6173
- Reproducibility of Results 5589
- Tomography, X-Ray Computed 4765
- Support Vector Machine 4678
- Computational Biology 4229
- Brain 4045
- Computer Simulation 3877
Recent Journals
- Scientific reports 5865
- Sensors (Basel, Switzerland) 4794
- PloS one 4011
- Neural networks : the official journal of the International Neural Network Society 2928
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1839
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1129
- Journal of chemical information and modeling 994
- Nature communications 938
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 850
- IEEE transactions on neural networks and learning systems 823